Bluebird Walmart By Express amp; American Faqs

Faker is a Python package that generates fake data for you. Whether you need to bootstrap your database, create good-looking XML documents, fill-in your persistence to stress test it, or anonymize data taken from a production service, Faker is for you.

You can check available Faker locales in the source code, under the providers package. The localization of Faker is an ongoing process, for which we need your help. Please don’t hesitate to create a localized provider for your own locale and submit a Pull Request (PR).

faker: is the script when installed in your environment, in development you could use python-mfaker instead

-h, --help: shows a help message

--version: shows the program’s version number

-oFILENAME: redirects the output to the specified filename

-l{bg_BG,cs_CZ,...,zh_CN,zh_TW}: allows use of a localized provider

-rREPEAT: will generate a specified number of outputs

-sSEP: will generate the specified separator after each generated output

-iBluebird Walmart By Express amp; American {my.custom_providerother.custom_provider} list of additional custom providers to use. Note that is the import path of the package containing your Provider class, not the custom Provider class itself.

fake: is the name of the fake to generate an output for, such as name, address, or text

[fakeargument...]: optional arguments to pass to the fake (e.g. the profile fake takes an optional list of comma separated field names as the first argument)

fromfakerimportFakerfake=Faker()# first, import a similar Provider or use the default onefromfaker.providersimportBaseProvider# create new provider class. Note that the class name _must_ be ``Provider``.classProvider(BaseProvider):deffoo(self):return'bar'# then add new provider to faker instancefake.add_provider(Provider)# now you can use:fake.foo()# 'bar'

By default all generators share the same instance of random.Random, which can be accessed with fromfaker.generatorimportrandom. Using this may be useful for plugins that want to affect all faker instances.

When using Faker for unit testing, you will often want to generate the same data set. For convenience, the generator also provide a seed() method, which seeds the shared random number generator. Calling the same methods with the same version of faker and seed produces the same results.

Please note that as we keep updating datasets, results are not guaranteed to be consistent across patch versions. If you hardcode results in your test, make sure you pinned the version of Faker down to the patch number.